{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Deep Learning Models -- A collection of various deep learning architectures, models, and tips for TensorFlow and PyTorch in Jupyter Notebooks.\n",
    "- Author: Sebastian Raschka\n",
    "- GitHub Repository: https://github.com/rasbt/deeplearning-models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Zoo --  Transfer Learning Example (VGG-16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sebastian Raschka \n",
      "\n",
      "CPython 3.6.8\n",
      "IPython 7.2.0\n",
      "\n",
      "torch 1.0.1.post2\n",
      "torchvision 0.2.2\n"
     ]
    }
   ],
   "source": [
    "%load_ext watermark\n",
    "%watermark -a 'Sebastian Raschka' -v -p torch,torchvision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import time\n",
    "import numpy as np\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "from torchvision import datasets\n",
    "from torchvision import transforms\n",
    "from torch.utils.data import DataLoader\n",
    "\n",
    "#######################################\n",
    "### PRE-TRAINED MODELS AVAILABLE HERE\n",
    "## https://pytorch.org/docs/stable/torchvision/models.html\n",
    "from torchvision import models\n",
    "#######################################\n",
    "\n",
    "if torch.cuda.is_available():\n",
    "    torch.backends.cudnn.deterministic = True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading an Example Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this example, we are going to work with CIFAR-10, because you are familiar with it and it is easier (smaller) than ImageNet. However, note that in a \"real-world application\", images with dimension > 224x224 are recommended. Here, we resize the images as a workaround\n",
    "\n",
    "- Note that due to the average pooling in the final layer, it is also possible to feed in 32x32-pixel images directly. However, I noticed that the performance is rather low (~65% test accuracy after 10 and 100 epochs)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Device: cuda:0\n",
      "Files already downloaded and verified\n",
      "Image batch dimensions: torch.Size([128, 3, 224, 224])\n",
      "Image label dimensions: torch.Size([128])\n"
     ]
    }
   ],
   "source": [
    "##########################\n",
    "### SETTINGS\n",
    "##########################\n",
    "\n",
    "# Device\n",
    "DEVICE = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "print('Device:', DEVICE)\n",
    "\n",
    "NUM_CLASSES = 10\n",
    "\n",
    "# Hyperparameters\n",
    "random_seed = 1\n",
    "learning_rate = 0.0001\n",
    "num_epochs = 10\n",
    "batch_size = 128\n",
    "\n",
    "\n",
    "##########################\n",
    "### MNIST DATASET\n",
    "##########################\n",
    "\n",
    "custom_transform = transforms.Compose([\n",
    "    transforms.Resize((224, 224)),\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize(mean=[0.485, 0.456, 0.406],\n",
    "                          std=[0.229, 0.224, 0.225])\n",
    "])\n",
    "\n",
    "## Note that this particular normalization scheme is\n",
    "## necessary since it was used for pre-training\n",
    "## the network on ImageNet.\n",
    "## These are the channel-means and standard deviations\n",
    "## for z-score normalization.\n",
    "\n",
    "\n",
    "train_dataset = datasets.CIFAR10(root='data', \n",
    "                                 train=True, \n",
    "                                 transform=custom_transform,\n",
    "                                 download=True)\n",
    "\n",
    "test_dataset = datasets.CIFAR10(root='data', \n",
    "                                train=False, \n",
    "                                transform=custom_transform)\n",
    "\n",
    "\n",
    "train_loader = DataLoader(dataset=train_dataset, \n",
    "                          batch_size=batch_size, \n",
    "                          num_workers=8,\n",
    "                          shuffle=True)\n",
    "\n",
    "test_loader = DataLoader(dataset=test_dataset, \n",
    "                         batch_size=batch_size, \n",
    "                         num_workers=8,\n",
    "                         shuffle=False)\n",
    "\n",
    "# Checking the dataset\n",
    "for images, labels in train_loader:  \n",
    "    print('Image batch dimensions:', images.shape)\n",
    "    print('Image label dimensions:', labels.shape)\n",
    "    break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading the Pre-Trained Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "VGG(\n",
       "  (features): Sequential(\n",
       "    (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (1): ReLU(inplace)\n",
       "    (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (3): ReLU(inplace)\n",
       "    (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "    (5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (6): ReLU(inplace)\n",
       "    (7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (8): ReLU(inplace)\n",
       "    (9): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "    (10): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (11): ReLU(inplace)\n",
       "    (12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (13): ReLU(inplace)\n",
       "    (14): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (15): ReLU(inplace)\n",
       "    (16): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "    (17): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (18): ReLU(inplace)\n",
       "    (19): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (20): ReLU(inplace)\n",
       "    (21): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (22): ReLU(inplace)\n",
       "    (23): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "    (24): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (25): ReLU(inplace)\n",
       "    (26): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (27): ReLU(inplace)\n",
       "    (28): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (29): ReLU(inplace)\n",
       "    (30): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "  )\n",
       "  (avgpool): AdaptiveAvgPool2d(output_size=(7, 7))\n",
       "  (classifier): Sequential(\n",
       "    (0): Linear(in_features=25088, out_features=4096, bias=True)\n",
       "    (1): ReLU(inplace)\n",
       "    (2): Dropout(p=0.5)\n",
       "    (3): Linear(in_features=4096, out_features=4096, bias=True)\n",
       "    (4): ReLU(inplace)\n",
       "    (5): Dropout(p=0.5)\n",
       "    (6): Linear(in_features=4096, out_features=1000, bias=True)\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = models.vgg16(pretrained=True)\n",
    "model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Freezing the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "for param in model.parameters():\n",
    "    param.requires_grad = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Assume we want to train the penultimate layer:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.classifier[3].requires_grad = True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, replace the output layer with your own output layer (here, we actually add two more output layers):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.classifier[6] = nn.Sequential(\n",
    "                      nn.Linear(4096, 512), \n",
    "                      nn.ReLU(), \n",
    "                      nn.Dropout(0.5),\n",
    "                      nn.Linear(512, NUM_CLASSES))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training (as usual)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = model.to(DEVICE)\n",
    "optimizer = torch.optim.Adam(model.parameters())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 001/010 | Batch 0000/0391 | Cost: 2.3338\n",
      "Epoch: 001/010 | Batch 0050/0391 | Cost: 0.6228\n",
      "Epoch: 001/010 | Batch 0100/0391 | Cost: 0.6328\n",
      "Epoch: 001/010 | Batch 0150/0391 | Cost: 0.6408\n",
      "Epoch: 001/010 | Batch 0200/0391 | Cost: 0.7256\n",
      "Epoch: 001/010 | Batch 0250/0391 | Cost: 0.6532\n",
      "Epoch: 001/010 | Batch 0300/0391 | Cost: 0.7125\n",
      "Epoch: 001/010 | Batch 0350/0391 | Cost: 0.6446\n",
      "Epoch: 001/010 | Train: 82.850% | Loss: 0.485\n",
      "Time elapsed: 10.31 min\n",
      "Epoch: 002/010 | Batch 0000/0391 | Cost: 0.6978\n",
      "Epoch: 002/010 | Batch 0050/0391 | Cost: 0.6642\n",
      "Epoch: 002/010 | Batch 0100/0391 | Cost: 0.5250\n",
      "Epoch: 002/010 | Batch 0150/0391 | Cost: 0.5881\n",
      "Epoch: 002/010 | Batch 0200/0391 | Cost: 0.5950\n",
      "Epoch: 002/010 | Batch 0250/0391 | Cost: 0.6431\n",
      "Epoch: 002/010 | Batch 0300/0391 | Cost: 0.6382\n",
      "Epoch: 002/010 | Batch 0350/0391 | Cost: 0.6368\n",
      "Epoch: 002/010 | Train: 84.794% | Loss: 0.439\n",
      "Time elapsed: 20.62 min\n",
      "Epoch: 003/010 | Batch 0000/0391 | Cost: 0.5730\n",
      "Epoch: 003/010 | Batch 0050/0391 | Cost: 0.4823\n",
      "Epoch: 003/010 | Batch 0100/0391 | Cost: 0.5051\n",
      "Epoch: 003/010 | Batch 0150/0391 | Cost: 0.4477\n",
      "Epoch: 003/010 | Batch 0200/0391 | Cost: 0.5656\n",
      "Epoch: 003/010 | Batch 0250/0391 | Cost: 0.6305\n",
      "Epoch: 003/010 | Batch 0300/0391 | Cost: 0.6265\n",
      "Epoch: 003/010 | Batch 0350/0391 | Cost: 0.6465\n",
      "Epoch: 003/010 | Train: 86.038% | Loss: 0.415\n",
      "Time elapsed: 30.93 min\n",
      "Epoch: 004/010 | Batch 0000/0391 | Cost: 0.5001\n",
      "Epoch: 004/010 | Batch 0050/0391 | Cost: 0.5504\n",
      "Epoch: 004/010 | Batch 0100/0391 | Cost: 0.5089\n",
      "Epoch: 004/010 | Batch 0150/0391 | Cost: 0.4440\n",
      "Epoch: 004/010 | Batch 0200/0391 | Cost: 0.5091\n",
      "Epoch: 004/010 | Batch 0250/0391 | Cost: 0.5236\n",
      "Epoch: 004/010 | Batch 0300/0391 | Cost: 0.4528\n",
      "Epoch: 004/010 | Batch 0350/0391 | Cost: 0.5743\n",
      "Epoch: 004/010 | Train: 86.296% | Loss: 0.390\n",
      "Time elapsed: 41.26 min\n",
      "Epoch: 005/010 | Batch 0000/0391 | Cost: 0.5132\n",
      "Epoch: 005/010 | Batch 0050/0391 | Cost: 0.4700\n",
      "Epoch: 005/010 | Batch 0100/0391 | Cost: 0.4637\n",
      "Epoch: 005/010 | Batch 0150/0391 | Cost: 0.5554\n",
      "Epoch: 005/010 | Batch 0200/0391 | Cost: 0.4977\n",
      "Epoch: 005/010 | Batch 0250/0391 | Cost: 0.4567\n",
      "Epoch: 005/010 | Batch 0300/0391 | Cost: 0.5479\n",
      "Epoch: 005/010 | Batch 0350/0391 | Cost: 0.5718\n",
      "Epoch: 005/010 | Train: 87.178% | Loss: 0.367\n",
      "Time elapsed: 51.57 min\n",
      "Epoch: 006/010 | Batch 0000/0391 | Cost: 0.4161\n",
      "Epoch: 006/010 | Batch 0050/0391 | Cost: 0.5224\n",
      "Epoch: 006/010 | Batch 0100/0391 | Cost: 0.4122\n",
      "Epoch: 006/010 | Batch 0150/0391 | Cost: 0.3939\n",
      "Epoch: 006/010 | Batch 0200/0391 | Cost: 0.6094\n",
      "Epoch: 006/010 | Batch 0250/0391 | Cost: 0.4580\n",
      "Epoch: 006/010 | Batch 0300/0391 | Cost: 0.6227\n",
      "Epoch: 006/010 | Batch 0350/0391 | Cost: 0.3779\n",
      "Epoch: 006/010 | Train: 87.560% | Loss: 0.373\n",
      "Time elapsed: 61.88 min\n",
      "Epoch: 007/010 | Batch 0000/0391 | Cost: 0.4695\n",
      "Epoch: 007/010 | Batch 0050/0391 | Cost: 0.4915\n",
      "Epoch: 007/010 | Batch 0100/0391 | Cost: 0.5667\n",
      "Epoch: 007/010 | Batch 0150/0391 | Cost: 0.6295\n",
      "Epoch: 007/010 | Batch 0200/0391 | Cost: 0.5733\n",
      "Epoch: 007/010 | Batch 0250/0391 | Cost: 0.4504\n",
      "Epoch: 007/010 | Batch 0300/0391 | Cost: 0.5983\n",
      "Epoch: 007/010 | Batch 0350/0391 | Cost: 0.5212\n",
      "Epoch: 007/010 | Train: 87.934% | Loss: 0.355\n",
      "Time elapsed: 72.19 min\n",
      "Epoch: 008/010 | Batch 0000/0391 | Cost: 0.3749\n",
      "Epoch: 008/010 | Batch 0050/0391 | Cost: 0.4332\n",
      "Epoch: 008/010 | Batch 0100/0391 | Cost: 0.5650\n",
      "Epoch: 008/010 | Batch 0150/0391 | Cost: 0.4598\n",
      "Epoch: 008/010 | Batch 0200/0391 | Cost: 0.5086\n",
      "Epoch: 008/010 | Batch 0250/0391 | Cost: 0.6177\n",
      "Epoch: 008/010 | Batch 0300/0391 | Cost: 0.5695\n",
      "Epoch: 008/010 | Batch 0350/0391 | Cost: 0.3319\n",
      "Epoch: 008/010 | Train: 88.056% | Loss: 0.349\n",
      "Time elapsed: 82.51 min\n",
      "Epoch: 009/010 | Batch 0000/0391 | Cost: 0.4925\n",
      "Epoch: 009/010 | Batch 0050/0391 | Cost: 0.4697\n",
      "Epoch: 009/010 | Batch 0100/0391 | Cost: 0.6494\n",
      "Epoch: 009/010 | Batch 0150/0391 | Cost: 0.4746\n",
      "Epoch: 009/010 | Batch 0200/0391 | Cost: 0.6954\n",
      "Epoch: 009/010 | Batch 0250/0391 | Cost: 0.3513\n",
      "Epoch: 009/010 | Batch 0300/0391 | Cost: 0.5205\n",
      "Epoch: 009/010 | Batch 0350/0391 | Cost: 0.4902\n",
      "Epoch: 009/010 | Train: 88.864% | Loss: 0.331\n",
      "Time elapsed: 92.78 min\n",
      "Epoch: 010/010 | Batch 0000/0391 | Cost: 0.6645\n",
      "Epoch: 010/010 | Batch 0050/0391 | Cost: 0.7019\n",
      "Epoch: 010/010 | Batch 0100/0391 | Cost: 0.3686\n",
      "Epoch: 010/010 | Batch 0150/0391 | Cost: 0.5411\n",
      "Epoch: 010/010 | Batch 0200/0391 | Cost: 0.4750\n",
      "Epoch: 010/010 | Batch 0250/0391 | Cost: 0.5674\n",
      "Epoch: 010/010 | Batch 0300/0391 | Cost: 0.5198\n",
      "Epoch: 010/010 | Batch 0350/0391 | Cost: 0.3849\n",
      "Epoch: 010/010 | Train: 88.656% | Loss: 0.325\n",
      "Time elapsed: 103.06 min\n",
      "Total Training Time: 103.06 min\n"
     ]
    }
   ],
   "source": [
    "def compute_accuracy(model, data_loader):\n",
    "    model.eval()\n",
    "    correct_pred, num_examples = 0, 0\n",
    "    for i, (features, targets) in enumerate(data_loader):\n",
    "            \n",
    "        features = features.to(DEVICE)\n",
    "        targets = targets.to(DEVICE)\n",
    "\n",
    "        logits = model(features)\n",
    "        _, predicted_labels = torch.max(logits, 1)\n",
    "        num_examples += targets.size(0)\n",
    "        correct_pred += (predicted_labels == targets).sum()\n",
    "    return correct_pred.float()/num_examples * 100\n",
    "\n",
    "\n",
    "def compute_epoch_loss(model, data_loader):\n",
    "    model.eval()\n",
    "    curr_loss, num_examples = 0., 0\n",
    "    with torch.no_grad():\n",
    "        for features, targets in data_loader:\n",
    "            features = features.to(DEVICE)\n",
    "            targets = targets.to(DEVICE)\n",
    "            logits = model(features)\n",
    "            loss = F.cross_entropy(logits, targets, reduction='sum')\n",
    "            num_examples += targets.size(0)\n",
    "            curr_loss += loss\n",
    "\n",
    "        curr_loss = curr_loss / num_examples\n",
    "        return curr_loss\n",
    "    \n",
    "    \n",
    "\n",
    "start_time = time.time()\n",
    "for epoch in range(num_epochs):\n",
    "    \n",
    "    model.train()\n",
    "    for batch_idx, (features, targets) in enumerate(train_loader):\n",
    "        \n",
    "        features = features.to(DEVICE)\n",
    "        targets = targets.to(DEVICE)\n",
    "            \n",
    "        ### FORWARD AND BACK PROP\n",
    "        logits = model(features)\n",
    "        cost = F.cross_entropy(logits, targets)\n",
    "        optimizer.zero_grad()\n",
    "        \n",
    "        cost.backward()\n",
    "        \n",
    "        ### UPDATE MODEL PARAMETERS\n",
    "        optimizer.step()\n",
    "        \n",
    "        ### LOGGING\n",
    "        if not batch_idx % 50:\n",
    "            print ('Epoch: %03d/%03d | Batch %04d/%04d | Cost: %.4f' \n",
    "                   %(epoch+1, num_epochs, batch_idx, \n",
    "                     len(train_loader), cost))\n",
    "\n",
    "    model.eval()\n",
    "    with torch.set_grad_enabled(False): # save memory during inference\n",
    "        print('Epoch: %03d/%03d | Train: %.3f%% | Loss: %.3f' % (\n",
    "              epoch+1, num_epochs, \n",
    "              compute_accuracy(model, train_loader),\n",
    "              compute_epoch_loss(model, train_loader)))\n",
    "\n",
    "\n",
    "    print('Time elapsed: %.2f min' % ((time.time() - start_time)/60))\n",
    "    \n",
    "print('Total Training Time: %.2f min' % ((time.time() - start_time)/60))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy: 84.23%\n"
     ]
    }
   ],
   "source": [
    "with torch.set_grad_enabled(False): # save memory during inference\n",
    "    print('Test accuracy: %.2f%%' % (compute_accuracy(model, test_loader)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "classes = ('plane', 'car', 'bird', 'cat',\n",
    "           'deer', 'dog', 'frog', 'horse', 'ship', 'truck')\n",
    "\n",
    "for batch_idx, (features, targets) in enumerate(test_loader):\n",
    "\n",
    "    features = features\n",
    "    targets = targets\n",
    "    break\n",
    "\n",
    "logits = model(features.to(DEVICE))\n",
    "_, predicted_labels = torch.max(logits, 1)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x180 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def unnormalize(tensor, mean, std):\n",
    "    for t, m, s in zip(tensor, mean, std):\n",
    "        t.mul_(s).add_(m)\n",
    "    return tensor\n",
    "\n",
    "n_images = 10\n",
    "\n",
    "fig, axes = plt.subplots(nrows=1, ncols=n_images, \n",
    "                         sharex=True, sharey=True, figsize=(20, 2.5))\n",
    "orig_images = features[:n_images]\n",
    "\n",
    "for i in range(n_images):\n",
    "    curr_img = orig_images[i].detach().to(torch.device('cpu'))\n",
    "    curr_img = unnormalize(curr_img,\n",
    "                           torch.tensor([0.485, 0.456, 0.406]),\n",
    "                           torch.tensor([0.229, 0.224, 0.225])) \n",
    "    curr_img = curr_img.permute((1, 2, 0))\n",
    "    axes[i].imshow(curr_img)\n",
    "    axes[i].set_title(classes[predicted_labels[i]])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
